Predictive Analysis and Email CategorizationUsing Large Language Models

Sabih Ahmed ()
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Sabih Ahmed: Department of Artificial Intelligence and Data Science (FAST-NUCES Karachi, Pakistan)

International Journal of Innovations in Science & Technology, 2024, vol. 6 Special Issue: 7, issue 7, 272-282

Abstract: With the global rise in internet users, email communication has become an integral part of daily life. Categorizing emails based on their intent can significantly save time and boost productivity. While previous research has explored machine learning models, including neural networks, for intent classification, Large Language Models (LLMs) have yet to be applied to intent-based email categorization. In this study, a subset of 11,000 emails from the publicly available Enron dataset was used to train various LLMs, including Bidirectional Encoder Representations from Transformers (BERT), Distil BERT, XLNet, and Generative Pre-training Transformer (GPT-2) for intent classification. Among these models, Distil BERT achieved the highest accuracy at 82%, followed closely by BERT with 81%. This research demonstrates the potential of LLMs to accurately identify the intent of emails, providing a valuable tool for email classification and management.

Keywords: BERT; DistilBERT; GPT-2; Large Language Based Models; Transformers; XL-Net (search for similar items in EconPapers)
Date: 2024
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